Immersive rooms are increasingly popular augmented reality systems that support multi-agent interactions within a virtual world. However, despite extensive content creation and technological developments, insights about perceptually-driven social dynamics, such as the complex movement patterns during virtual world navigation, remain largely underexplored. Computational models of motion dynamics can help us understand the underlying mechanism of human interaction in immersive rooms and develop applications that better support spatially distributed interaction. In this work, we propose a new agent-based model of emergent human motion dynamics. The model represents human agents as simple spatial geometries in the room that relocate and reorient themselves based on the salient virtual spatial objects they approach. Agent motion is modeled as an interactive process combining external diffusion-driven influences from the environment with internal self-propelling interactions among agents. Further, we leverage simulation-based inference (SBI) to show that the governing parameters of motion patterns can be estimated from simple observables. Our results indicate that the model successfully captures action-related agent properties but exposes local non-identifiability linked to environmental awareness. We argue that our simulation-based approach paves the way for creating adaptive, responsive immersive rooms -- spaces that adjust their interfaces and interactions based on human collective movement patterns and spatial attention.

翻译：沉浸式房间作为增强现实系统日益普及，能够在虚拟世界中支持多智能体交互。然而，尽管内容创作与技术发展已相当深入，关于感知驱动的社会动力学（例如虚拟世界导航过程中的复杂运动模式）的认知仍很大程度上未被充分探索。运动动力学的计算模型有助于我们理解沉浸式房间中人类交互的内在机制，并开发出能更好支持空间分布式交互的应用。在本研究中，我们提出了一种新的基于智能体的涌现式人类运动动力学模型。该模型将人类智能体表示为房间内的简单空间几何体，这些几何体根据其接近的显著虚拟空间对象进行位置重定向与朝向调整。智能体运动被建模为一个交互过程，结合了来自环境的外部扩散驱动影响与智能体之间的内部自推进相互作用。此外，我们利用基于模拟的推断（SBI）证明，运动模式的控制参数可通过简单观测值进行估计。我们的结果表明，该模型成功捕捉了与动作相关的智能体属性，但也揭示了与环境意识相关的局部不可辨识性。我们认为，这种基于模拟的方法为创建自适应、响应式的沉浸式房间——即能够根据人类集体运动模式与空间注意力调整其界面与交互的空间——开辟了道路。